جست‌وجوی استاد و پژوهشگر

David Burgessمشاهده پروفایل

استاد

• Space and Astrophysical Plasma Physics
• Collisionless Plasma Shocks
• Plasma Turbulence
• +۳ مورد دیگر

David Burgess is a Professor of Mathematics and Astronomy at Queen Mary University of London, affiliated with the School of Physical and Chemical Sciences and the Centre for Fundamental Physics. His research focuses on space and astrophysical plasma physics, particularly shocks in collisionless plasmas, turbulence, particle acceleration, and large-scale simulations. He collaborates internationally with ESA and NASA mission groups. Key Research Areas: Solar wind turbulence, bow shock dynamics, interplanetary shocks, and plasma simulations. Grants: £868k from STFC for heliospheric research (2023–2027), £626k for planetary studies (2020–2024). Collaborations: Involves groups in Europe and the USA, leveraging data from space missions like Solar Orbiter and MMS. His work uses particle-in-cell simulations and hybrid models to study plasma behavior, with recent emphasis on magnetic reconnection, electron heating, and shock-turbulence interactions.

Gert Brodinمشاهده پروفایل

استاد

• Plasma Physics
• Quantum Plasma Dynamics
• High-Intensity Laser Plasmas
• +۳ مورد دیگر

Professor Gert Brodin is a faculty member at the Department of Physics, Umeå universitet, serving as Deputy Head of Department and Assistant Head of Department. His research focuses on plasma theory, particularly in regimes where quantum mechanics and quantum electrodynamics (QED) intersect with plasma dynamics. Key areas include quantum plasmas in high-density environments, relativistic plasmas under ultrastrong electromagnetic fields, and nonlinear wave phenomena. He leads the Plasma Theory research group, exploring topics such as pair production in vacuum/plasma, QED effects in high-intensity laser interactions, and relativistic kinetic theory for spin-1/2 particles. His work employs advanced methods like von Neumann equations for density matrices, Wigner transformations, and the Dirac-Heisenberg-Wigner formalism. Recent publications address semiclassical theories in strong-field plasmas, relativistic Landau quantization, and radiation reaction effects. Brodin has collaborated extensively with researchers such as Haidar Al-Naseri and Jens Zamanian, advancing theoretical frameworks for quantum plasma dynamics. His research group’s projects include studying plasma behavior at the Schwinger limit, electron-acoustic wave damping via multi-plasmon resonances, and ultrafast electron hole dynamics. Brodin holds a Docent qualification and has contributed to influential journals like Physical Review E , Physics of Plasmas , and Reviews of Modern Plasma Physics .

Ahmedullah Azizمشاهده پروفایل

استادیار

• Mixed-Signal VLSI Circuits
• Non-Volatile Memory
• Beyond CMOS Device Design
• +۴ مورد دیگر

Ahmedullah Aziz is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He earned his Ph.D. in Electrical and Computer Engineering from Purdue University (2019), an M.S. from Pennsylvania State University (2016), and a B.S. from Bangladesh University of Engineering & Technology (2013). His research focuses on mixed-signal VLSI circuits, non-volatile memory, and beyond-CMOS device design, emphasizing emerging technologies like ferroelectrics and spintronics. He leads the NorDIC Lab and has published over 60 articles in journals, conferences, and patents. Education: PhD, Electrical & Computer Engineering, Purdue University, 2019 MS, Electrical Engineering, Pennsylvania State University, 2016 BS, Electrical & Electronic Engineering, BUET, 2013 Research Interests: Aziz explores device-circuit-system co-design techniques, particularly in cryogenic neuromorphic systems, superconducting memory, and ferroelectric-based circuits. His work bridges material innovation with practical applications, aiming to advance energy-efficient and high-performance computing. Awards: EDAA Outstanding Dissertation Award (2019/2020) Outstanding Graduate Student Research Award (Purdue, 2019) Samsung 'Icon' Award (2013) Best Publication Awards (SRC-DARPA STARnet, 2015/2016) Advising & Grants: While specific grants are not detailed, his research has been supported by prestigious institutions. He has advised/co-advised projects in emerging technologies but no listed students. His work extends to reviewing IEEE journals, conference TPC roles, and editorial contributions. Labs & Teams: He directs the NorDIC Lab, focusing on next-generation computing and device innovation.

Dong S. Haمشاهده پروفایل

استاد

• Analog IC Design
• RF ICs
• Power Management Circuits
• +۷ مورد دیگر

Dong S. Ha is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. As Founding Director of the Multifunctional Integrated Circuits & Systems (MICS) Lab, he focuses on advanced circuit design for energy harvesting, RF systems, and high-temperature electronics. His work spans analog/RF ICs, power management circuits, and wireless IoT solutions with machine learning integration. Education: PhD (1986) and MS (1984) in Electrical and Computer Engineering from the University of Iowa; B.S. (1974) in Electrical Engineering from Seoul National University. Research interests include energy harvesting (piezoelectric, thermal, RF), high-temperature RF circuits for oil/gas/spacecraft applications, and smart IoT systems. He actively seeks students for projects in RF design, energy harvesting, and embedded systems. His lab develops cutting-edge solutions for harsh environment communication, sustainable energy systems, and smart agriculture monitoring. Awarded IEEE Fellow (2008) for contributions to VLSI design/test. Key publications span energy harvesting circuits, GaN-based high-temperature systems, and low-power IoT architectures. Current projects include self-sustaining smart farm networks using federated learning and attack-resistant sensor systems. Labs/Teams: Leads MICS Lab focusing on integrated circuits and systems. Collaborates on cross-disciplinary projects involving machine learning, embedded systems, and sustainable energy. Active in industry partnerships for aerospace and automotive applications.

George Papadakisمشاهده پروفایل

استاد

• Aerodynamics
• Fluid Dynamics
• Turbulence
• +۵ مورد دیگر

Professor George Papadakis is a Professor of Aerodynamics at the Department of Aeronautics, Faculty of Engineering at Imperial College London. His research focuses on fundamental analysis and manipulation of transitional or turbulent flows, with applications in aerodynamics, flow control, and mixing enhancement. He leads the Papadakis Lab, which develops computational methods and optimization algorithms for fluid dynamics problems. Education: PhD in Mechanical Engineering (National Technical University of Athens, 1996), BEng in Mechanical Engineering (National Technical University of Athens, 1990). Professional history includes roles as Lecturer at King's College London (1999–2011) and Reader at Imperial College (2011–present). Research interests include turbulence enhancement/suppression, sensitivity analysis of chaotic systems, and DNS/LES simulations. His group is funded by EPSRC, European Union, Leverhulme Trust, and the President's Scholarship Fund. Key affiliations include the Energy Futures Lab and Flow Control networks. Advising and grants: Supervised numerous PhD students (e.g., Dandan Xiao, Felipe Alves Portela) and secured funding from multiple agencies. Current students include Hanxun Yao, Karim Shawki, and others. Research outputs span flow control, vortex dynamics, and industrial mixing applications. Labs/teams: Papadakis Lab focuses on aerodynamics, turbulence, and computational methods. Collaborations include Temasek Labs (Singapore) and Prof. J.C. Vassilicos (Imperial College).

• Machine Learning
• Representation Learning
• Geometric Deep Learning
• +۴ مورد دیگر

Siamak Ravanbakhsh is an Associate Professor at McGill University's School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with an emphasis on geometry, symmetry, and probabilistic inference. He has held academic positions at the University of British Columbia and was a postdoctoral fellow at Carnegie Mellon University. Education: B.Sc. in Computer Science, Sharif University of Technology M.Sc. and Ph.D. in Computer Science, University of Alberta (supervised by Russ Greiner) Postdoctoral Fellowship at Carnegie Mellon University (with Barnabás Póczos and Jeff Schneider) His research interests span geometric deep learning, equivariant networks, reinforcement learning, and AI for scientific applications. Notable contributions include work on symmetry-aware models, diffusion processes, and equivariant representation learning. Publications highlight advancements in causal abstraction, diffusion-based anomaly detection, and equivariant architectures for crystals and hierarchical structures. His work often bridges theory and application, emphasizing symmetry principles. Advising & Grants: Supervised over 20 graduate students and postdocs, including recent PhD graduates Daniel Levy and Mehran Shakerinava Active in mentoring M.Sc. and internship students He contributes to academic leadership roles at Mila and McGill, fostering interdisciplinary collaborations in AI research.

• Geotechnics
• Numerical Simulations
• Bayesian Inference
• +۴ مورد دیگر

Stéphane Commend is an Associate HES Professor at the Fribourg School of Engineering and Architecture (HEIA-FR) under HES-SO Valais-Wallis. He also holds a lecturer role at the School of Engineering and Management of the Canton of Vaud. His primary research focuses on geotechnics, numerical simulations, and probabilistic modeling applied to infrastructure projects like tunneling and deep excavations. Education and affiliations include roles across multiple HES-SO institutions, with a strong emphasis on integrating advanced computational methods into geotechnical engineering. Notable projects include the Grand Paris Express tunnel project, Bayesian inference for wood constitutive modeling, and probabilistic risk analysis for urban construction. Research interests span soil-structure interaction, finite element modeling, and uncertainty quantification. Recent work emphasizes Bayesian methods for parameter calibration, machine learning in excavation design, and natural hazard vulnerability assessment. Key contributions include frameworks linking ZSOIL and UQLab for reliability analysis, and prototypes like SLIDE-PM for mudflow impact modeling. Current projects (e.g., iBAG and OptiSoil) focus on optimizing construction methods using AI and data-driven approaches. He leads collaborative teams across HES-SO institutes and academic partners like EPFL and CETU. Key Projects: iBAG Project (2022–2025): Bayesian methods in geotechnics OptiSoil (2019–2025): Machine learning for excavation design TULIP Project: TBM-pile interaction probabilistic analysis

James R. Greenمشاهده پروفایل

استاد

• Machine Learning
• Biomedical Informatics
• Computational Biology
• +۹ مورد دیگر

James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University , where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing. His educational background includes: B.A.Sc. in Systems Design Engineering, University of Waterloo (1998) M.Sc.(Eng.), Queen's University (2000) PhD, Queen's University (2005) Dr. Green's research focuses on machine learning challenges in biomedical informatics , particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO. His recent publications reflect a strong trend in computational biology and machine learning , with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM. Scientific and teaching recognitions include: Three teaching awards NSERC Best Project Award (twice: 2006-2007 and 2007-2008) Multiple student projects resulting in conference papers (e.g., CMBEC) He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration. Lab and research team initiatives include: Development of open-access web servers for protein analysis Collaborations with biologists and clinicians Integration of hardware and software for medical applications

Solomon G. Diamondمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Functional Neuroimaging
• Magnetic Nanoparticle Imaging
• +۳ مورد دیگر

Solomon G. Diamond is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering, serving as Co-Director of the Design Initiative at Dartmouth. He holds degrees from Dartmouth (AB 1997, BE 1998) and Harvard (SM 2001, PhD 2004). His research focuses on biomedical imaging, functional neuroimaging, and magnetic nanoparticle imaging, with emphasis on diagnostic technologies and medical device development. He has received awards including the 2023 Outstanding Service Award and the 2002 Derek Bok Teaching Award. Research projects include neurovascular coupling studies, clinical optical-electric probes, and magnetic nanoparticle imaging. He co-founded Lodestone Biomedical, a medtech company advancing nanoparticle-based biosensors. His work bridges engineering and medicine, with notable contributions to magnetic nanoparticle characterization and imaging array systems. He teaches courses like ENGS 90 (Engineering Design Methodology) and ENGS 29 (Computer-Aided Design & Kinematics). Recent work includes a 2024 study on salt concentration effects in magnetic nanoparticle biosensors with PhD candidate Gabby Moss. He holds patents for technologies like magnetic susceptibility tomography and in-bed exercise machines. His interdisciplinary collaborations span biomedical engineering, materials science, and clinical diagnostics.

• Microsystems Technology
• MEMS (Micro-Electro-Mechanical Systems)
• Terahertz Technology
• +۳ مورد دیگر

Prof. Dr.-Ing. Martin Hoffmann is a Professor of Microsystems Technology at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum. His academic career began at the University of Dortmund, where he earned his doctorate in high-frequency technology and later habilitated in microsystems technology (2003). He held roles as a private lecturer and industry researcher before becoming a university professor at TU Ilmenau (2006). He joined Ruhr University in 2017, specializing in cutting-edge microsystems research. His research focuses on MEMS, THz technology, microactuators, and nanoimprint lithography. Key projects include cooperative microactuator systems, THz biosensors, and energy-autonomous sensors. He collaborates with institutions like TU Ilmenau, Purdue University, and Nagoya University through international programs like Double Degree and Erasmus. His work spans academic advising, grants, and industry partnerships (e.g., HL Planartechnik GmbH, Silicon Manufacturing Itzehoe GmbH). Notable contributions include silicon grass nanostructuring, palladium-based gas sensors, and wafer-scale MoS₂ deposition. His lab develops micromechanical systems for biomedical, environmental, and defense applications.

Didier Sornetteمشاهده پروفایل

استاد

• Complex Systems
• Risk Analysis
• Financial Crises and Bubbles
• +۱۱ مورد دیگر

Didier Sornette is Professor of Entrepreneurial Risks at the Department of Management, Technology and Economics (D-MTEC), Swiss Federal Institute of Technology Zurich (ETH Zurich). He is also Professor of Finance at the Swiss Finance Institute, Associate Member of the Department of Physics and Department of Earth Sciences at ETH Zurich, Principal Investigator at the Future Resilient Systems Center at the National University of Singapore, and Specially Appointed Professor at the Institute of Innovative Research, Tokyo Institute of Technology. His research centers on the theory and application of complex systems, particularly focusing on the prediction and control of extreme events—termed 'dragon-kings'—in financial markets, geophysical systems, and socio-economic networks. He employs nonlinear dynamical models, statistical analysis, and feedback mechanisms to detect early warning signals of crises. The available publication highlights his work at the intersection of complexity science and democratic governance, suggesting a growing interest in applying systemic risk methodologies to societal structures. This reflects a broad trend in his research: leveraging quantitative models for real-world systemic instabilities across disciplines. Director, Financial Crisis Observatory (FCO) Co-founder, ETH Risk Center Principal Investigator, Future Resilient Systems, National University of Singapore Specially Appointed Professor, Tokyo Institute of Technology He leads interdisciplinary teams working on InnovWiki, a collaborative platform integrating idea development with prediction markets and data visualization. His lab initiatives emphasize open collaboration, crowd-based evaluation, and real-time risk monitoring. While specific awards and students are not listed in the provided text, his leadership in major research centers and global observatories underscores significant recognition and mentorship activity.

Mika Sillanpääمشاهده پروفایل

استاد

• Quantum Technology
• Nanophysics
• Microwave Optomechanics
• +۵ مورد دیگر

Mika Sillanpää is a Professor at the Department of Applied Physics , Aalto University , and serves as a Professor (Associate Professor) at the Centre of Excellence in Quantum Technology and the Quantum Nanomechanics group. His research focuses on experimental superconducting nanoelectronics at milli-Kelvin temperatures, particularly Josephson junction quantum circuits and microwave optomechanics. Doctoral degree from Helsinki University of Technology (2005) Master's degree from Helsinki University of Technology (1999) Research Interests: Prof. Sillanpää has pioneered low-noise amplification schemes using microwave optomechanics and demonstrated quantum squeezing/entanglement in micromechanical devices. Key projects include GUANTUM (quantum-gravity limits) and MQSens (quantum sensing with mechanical resonators). Scientific Contributions: His work spans quantum back-action evading measurements, noiseless amplification, and TLS detection in resonators. Recent publications highlight Casimir force measurements between superconductors and long-lived electromechanical systems using silicon-carbide membranes. ERC Consolidator Grant (2013) ERC Starting Grant (2009) IUPAP C5 Young Scientist Prize (2011) Väisälä Prize (2015)

Ali Coruhمشاهده پروفایل

استاد

• Condensed Matter Physics
• Nanotechnology
• Materials Science
• +۳ مورد دیگر

Prof. Dr. Ali Coruh is a distinguished faculty member at Sakarya University 's Faculty of Science, Department of Physics , where he has served as Head of the General Physics Department since 2022. With a career spanning over three decades, his expertise lies in Condensed Matter Physics , Nanotechnology , and Materials Science , focusing on perovskite oxides , adsorbent materials , and supercapacitor applications . PhD in Physics, Middle East Technical University (2003) MSc in Physics, Sakarya University (1996) BSc in Physics, Karadeniz Technical University (1989) His research interests include computational modeling of molecular structures, environmental pollutant removal using nanomaterials, and electrochemical properties of advanced materials. Recent studies focus on pharmaceutical pollutant recovery via organoclay composites, Pd-doped perovskites for biosensing, and perovskite oxides for energy storage. His work bridges theoretical and experimental approaches in solid-state physics and environmental remediation . The article trends show consistent contributions to Journal of the Taiwan Institute of Chemical Engineers , Chemical Engineering Journal , and Journal of Energy Storage , with a focus on adsorption mechanisms , electrocatalysis , and nanocomposite synthesis . Collaborations span international institutions, including studies on supercapacitor electrode materials and microwave absorbing textiles . As an academic leader, he has supervised 14 doctoral and master's theses , including works on molecular dynamics , perovskite oxides , and nanoparticle-based sensors . He has also served on editorial boards for journals like Materials Chemistry and Physics (2010-2013) and contributed to TUBITAK Olympiad training .

Ellen Rathjeمشاهده پروفایل

استاد

• Geotechnical Engineering
• Earthquake Engineering
• Seismic Response Analysis
• +۵ مورد دیگر

Ellen Rathje is a Professor and Janet S. Cockrell Centennial Chair in Engineering at the University of Texas at Austin's Department of Civil, Architectural, and Environmental Engineering within the Cockrell School of Engineering. Her expertise spans geotechnical engineering with a focus on earthquake engineering, seismic response of earth structures, and liquefaction evaluation. She leads research initiatives such as DesignSafe cyberinfrastructure and TexNet Seismological Network, advancing geohazard risk assessment and computational modeling. Education: Ph.D. (1997), M.S. (1994), and B.S. (1993) in Civil Engineering from University of California, Berkeley and Cornell University. Research Interests: Dr. Rathje investigates earthquake-induced ground failures, including lateral spreading, liquefaction, and slope instabilities. Her work integrates machine learning, finite element analysis, and geospatial data to enhance predictive models for infrastructure resilience. Recent projects address induced seismicity in energy-producing regions, site amplification in Central and Eastern North America, and tailings dam failure mechanisms. Awards: Recipient of the 2022 Ralph B. Peck Award, 2018 William B. Joyner Lecture Award, and 2016 ASCE Fellow distinction. Her work has advanced open science through DesignSafe's cyberinfrastructure, supporting natural hazards research collaboration. Technical Contributions: Developed hybrid finite-element/material point methods for granular collapse modeling, probabilistic frameworks for regional landslide assessments, and neural network-based site amplification models. Active in post-earthquake reconnaissance through GEER (Geotechnical Extreme Events Reconnaissance) and TexNet operations.

• Adaptive and robust control
• Nonlinear control
• Precision control
• +۴ مورد دیگر

Bin Yao is a Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He holds positions within the School of Mechanical Engineering and has affiliations with Purdue's broader Engineering College. His research focuses on adaptive and robust control systems, nonlinear control, precision mechanical systems, vehicle control, and robotics, with applications in human-machine interaction, transportation, and advanced robotics. Yao earned his B.Eng. from Beijing University of Aeronautics & Astronautics (1987), M.Eng. from Nanyang Technological University (1992), and Ph.D. from UC Berkeley (1996). He has received numerous awards, including the 2010 Changjiang Chair Professorship and the 2007 ASME Outstanding Young Investigator Award. His work emphasizes theoretical advancements in control systems and their practical implementation in industrial and robotic applications. Key research trends in his publications include precision motion control of gantry systems, adaptive strategies for hydraulic manipulators, and teleoperation systems with haptic feedback. His articles often bridge theoretical control methodologies with real-world mechanical systems, emphasizing robustness and adaptability. Awards: Over 10 prestigious awards spanning from 1985 to 2010, including international recognitions in control systems and academic excellence. Grants: Secured major funding through NSF CAREER Award (1998) and other industrial collaborations. His research lab focuses on advanced control systems integration in mechanical and robotic platforms, with ongoing projects in precision manufacturing and autonomous vehicle dynamics.